Context:
AI chips with their hardware architectures and complementary packaging, memory, storage and interconnect technologies, make it possible to infuse AI into a broad spectrum of applications to help turn data into information and then into knowledge.
Relevance:
GS III- Science and Technology
Dimensions of the Article
- What are AI chips?
- Difference between traditional chips and AI chips
- Applications
What are AI chips?
AI chips are built with specific architecture and have integrated AI acceleration to support deep learning-based applications.
There are different types of AI chips designed for diverse AI applications:
- Application-specific integrated circuits (ASICs),
- Field-programmable gate arrays (FPGAs),
- Central processing units (CPUs) and GPUs,
Deep Learning:
- Deep learning, more commonly known as active neural network (ANN) or deep neural network (DNN), is a subset of machine learning and comes under the broader umbrella of AI.
- It combines a series of computer commands or algorithms that stimulate activity and brain structure.
- DNNs go through a training phase, learning new capabilities from existing data.
- DNNs can then inference, by applying these capabilities learned during deep learning training to make predictions against previously unseen data.
- Deep learning can make the process of collecting, analysing, and interpreting enormous amounts of data faster and easier.
Difference between traditional chips and AI chips
Traditional Chips:
- It contain processor cores and memory, perform computational tasks, they continuously move commands and data between the two hardware components.
- These chips, however, are not ideal for AI applications as they would not be able to handle higher computational necessities of AI workloads which have huge volumes of data.
- Although, some of the higher-end traditional chips may be able to process certain AI applications.
AI chips
- It generally contain processor cores as well as several AI-optimised cores (depending on the scale of the chip) that are designed to work in harmony when performing computational tasks.
- The AI cores are optimised for the demands of heterogeneous enterprise-class AI workloads with low-latency inferencing, due to close integration with the other processor cores, which are designed to handle non-AI applications.
- AI chips, essentially, reimagine traditional chips’ architecture, enabling smart devices to perform sophisticated deep learning tasks such as object detection and segmentation in real-time, with minimal power consumption.
Applications:
- Semiconductor firms have developed various specialised AI chips for a multitude of smart machines and devices, including ones that are said to deliver the performance of a data centre-class computer to edge devices.
- Some of these chips support in-vehicle computers to run state-of-the-art AI applications more efficiently.
- AI chips are also powering applications of computational imaging in wearable electronics, drones, and robots.
- Additionally, the use of AI chips for NLP (Natural language processing) applications has increased due to the rise in demand for chatbots and online channels such as Messenger, Slack, and others.
- They use NLP to analyse user messages and conversational logic.
- Then there are chipmakers who have built AI processors with on-chip hardware acceleration, designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions.
- As AI becomes pervasive across different workloads, having a dedicated inference accelerator that includes support for major deep learning frameworks would allow companies to harness the full potential of their data.
-Source: The Hindu
